Charasteristic function of integer valued distribution

In summary, the conversation discusses how to prove that if $\varphi$ is the characteristic function of an integer valued distribution, then the probability mass function can be computed using the formula $ p(k) = \frac{1}{2\pi} \cdot \int^{\pi}_{-\pi} e^{-ikt}\varphi(t) dt \;,\forall k \in \mathbb{Z} $. The key to proving this is knowing that $\int_{-\pi}^{\pi} e^{-ikt}e^{ilt}\; dt=2 \pi$ if $k=l$ and $0$ otherwise. By changing the order of integration and summation, the desired result can be obtained.
  • #1
bennyzadir
18
0
How to prove that if $\varphi$ is the characteristic function of an integer valued distribution, then the probability mass function can be computed as
$ p(k) = \frac{1}{2\pi} \cdot \int^{\pi}_{-\pi} e^{-ikt}\varphi(t) dt \;,\forall k \in \mathbb{Z} $

I would be really grateful if you could help me.
 
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  • #2
zadir said:
How to prove that if $\varphi$ is the characteristic function of an integer valued distribution, then the probability mass function can be computed as
$ p(k) = \frac{1}{2\pi} \cdot \int^{\pi}_{-\pi} e^{-ikt}\varphi(t) dt \;,\forall k \in \mathbb{Z} $

I would be really grateful if you could help me.

You need to know that:

\( \displaystyle \int_{-\pi}^{\pi} e^{-ikt}e^{ilt}\; dt=2 \pi\) if \(k=l\) and \(0\) otherwise.

Then you just change the order of integration and summation in \( \int_{-pi}^{\pi} e^{-kt}\phi(t)\; dt\) to get the required result.

CB
 
  • #3
CaptainBlack said:
You need to know that:

\( \displaystyle \int_{-\pi}^{\pi} e^{-ikt}e^{ilt}\; dt=2 \pi\) if \(k=l\) and \(0\) otherwise.

Then you just change the order of integration and summation in \( \int_{-pi}^{\pi} e^{-kt}\phi(t)\; dt\) to get the required result.

CB

Thank you so much!
 

FAQ: Charasteristic function of integer valued distribution

What is a characteristic function of an integer valued distribution?

A characteristic function is a mathematical tool used to describe the probability distribution of a random variable. In the case of an integer valued distribution, the characteristic function is a function that maps the possible integer values to their corresponding probabilities.

How is a characteristic function different from a probability mass function?

While both the characteristic function and the probability mass function describe the probability distribution of a random variable, they differ in their approach. A probability mass function directly assigns probabilities to each possible value, while a characteristic function describes the distribution through a mathematical function.

What are the main uses of a characteristic function?

A characteristic function has various uses, including calculating moments of a distribution, finding the distribution of a sum of independent random variables, and testing for independence of random variables.

Can a characteristic function be used to describe continuous distributions?

Yes, a characteristic function can be used to describe both discrete and continuous distributions. However, for continuous distributions, the characteristic function is defined as the Fourier transform of the probability density function.

How is the characteristic function related to the cumulative distribution function?

The characteristic function and the cumulative distribution function are related through the inverse Fourier transform. The characteristic function is the Fourier transform of the probability density function, and the cumulative distribution function is the inverse Fourier transform of the characteristic function.

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